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Data-Driven Economic Agent-Based Models

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  • Marco Pangallo
  • R. Maria del Rio-Chanona

Abstract

Economic agent-based models (ABMs) are becoming more and more data-driven, establishing themselves as increasingly valuable tools for economic research and policymaking. We propose to classify the extent to which an ABM is data-driven based on whether agent-level quantities are initialized from real-world micro-data and whether the ABM's dynamics track empirical time series. This paper discusses how making ABMs data-driven helps overcome limitations of traditional ABMs and makes ABMs a stronger alternative to equilibrium models. We review state-of-the-art methods in parameter calibration, initialization, and data assimilation, and then present successful applications that have generated new scientific knowledge and informed policy decisions. This paper serves as a manifesto for data-driven ABMs, introducing a definition and classification and outlining the state of the field, and as a guide for those new to the field.

Suggested Citation

  • Marco Pangallo & R. Maria del Rio-Chanona, 2024. "Data-Driven Economic Agent-Based Models," Papers 2412.16591, arXiv.org.
    • Handle: RePEc:arx:papers:2412.16591
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    References listed on IDEAS

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